Data Sharing and Compression for Cooperative Networked Control

TL;DR

This paper introduces a method for creating highly compressed, task-specific forecasts for networked control systems, significantly reducing data transmission while improving control performance.

Contribution

It proposes a co-design approach for forecasts and controllers, optimizing for control objectives rather than just prediction accuracy, with theoretical and empirical validation.

Findings

Achieves at least 25% improvement in control performance

Reduces data transmission by 80% compared to existing methods

Provides theoretical compression bounds for networked LQR control

Abstract

Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation. Typically, forecasts are designed without knowledge of a downstream controller's task objective, and thus simply optimize for mean prediction error. However, such task-agnostic representations are often too large to stream over a communication network and do not emphasize salient temporal features for cooperative control. This paper presents a solution to learn succinct, highly-compressed forecasts that are co-designed with a modular controller's task objective. Our simulations with real cellular, Internet-of-Things (IoT), and electricity load data show we can improve a model predictive controller's performance by at least $25\%$ while transmitting $80\%$ less data than the competing method. Further, we present theoretical compression results for a networked variant of the classical linear quadratic regulator (LQR) control problem.